论文标题
使用人工神经网络和R-X阻抗图估算电气传输线中高阻抗故障位置
Estimation of High Impedance Fault Location in Electrical Transmission Lines Using Artificial Neural Networks and R-X Impedance Graph
论文作者
论文摘要
确保从发电到向城市传播的过程中的连续性非常重要。系统中最重要的部分是能源传输线和保护这些线的距离保护。在系统短路的情况下，应尽快将电气安装中保护继电器的主要功能停用。需要准确的错误位置技术来快速有效地工作。距离继电器被广泛用作传输和分销线路中的主要和备份保护。基本上，距离保护继电器通过比较电压和电流值来确定线路的阻抗。在这项研究中，人工神经网络（ANN）已被用来准确定位154 kV功率传输线中的高阻抗短路故障。断路器，电流 - 电压变压器，架空传输线，距离保护继电器和距离保护继电器的阻抗图（R-X）是通过使用仿真程序形成的，以使研究真实。通过记录高阻抗短路故障时发生阻抗发生变化的图像而创建的数据集。图像中的相关焦点作为对不同ANN模型的输入，并预测在传输线上的不同位置的短路故障，其精度很高。
It is very important to ensure continuity in the process from generation of electricity to transmission to cities. The most important part of the system is energy transmission lines and distance protection relays that protect these lines. The main function of the protection relays in electrical installations should be deactivated as soon as possible in the event of short circuits in the system. An accurate error location technique is required to make fast and efficient work. Distance relays are widely used as main and backup protection in transmission and distribution lines. Basically, distance protection relays determine the impedance of the line by comparing the voltage and current values. In this study, artificial neural network (ANN) has been used to accurately locate high impedance short circuit faults in 154 kV power transmission lines. The impedance diagram (R-X) of the circuit breaker, current-voltage transformer, overhead transmission line, distance protection relay and distance protection relay has been formed by using simulation program in order to make the study real. The data sets created by recording the image of the change of the impedance occurring at the time of high impedance short circuit fault. The related focal points in the images are given as input to different ANN models and predicted the short circuit faults occurring at different locations on the transmission lines with high accuracy.
